The present invention relates generally to methods and systems of predicting fatigue life in aluminum castings, and more particularly to predicting fatigue life of a cast aluminum object by combining extreme value statistics and multiscale fatigue life models with casting flaw and microstructural constituent types, sizes and shapes.
Improved fuel efficiency is an important goal in automotive design. One way to help achieve this goal is through the use of lightweight materials in the construction of vehicle component parts, including in the powertrain and related componentry. In addition to making such components lighter, it is desirable to keep their cost of production low, through the use of casting and related scalable processes. For example, aluminum based materials and related methods of casting may be employed where heretofore heavy materials (typically, steel or other iron based alloys) have been used.
Nevertheless, care must be taken when casting certain lightweight materials, as such materials may be susceptible to failure by fatigue, where component failure proceeds over various stages, starting with microcrack incubation, small and long crack propagation leading to either a leak (in the case of containment vessels), final overload, or other loss of function. It is accordingly desirable that methods and systems be developed to accurately predict fatigue properties of these castings early in the component and manufacturing process design cycle. There are two fatigue design philosophies for structural components—infinite life and damage tolerant design. Infinite life design does not allow crack initiation and propagation under service loading, while damage tolerant design assumes the presence of casting imperfections, and permits crack propagation. For aluminum shape castings, the presence of casting flaws and discontinuities is almost inevitable. Furthermore, below the gigacycle life regime, there is no apparent fatigue endurance limit for cast aluminum alloys. Therefore, the damage tolerant design approach, which may produce more structurally efficient designs than the infinite life approach, is appropriate for fatigue loaded aluminum shape castings.
Fatigue properties of cast aluminum components are strongly dependent upon flaws, such as voids and related porosity, or the formation of oxide films or the like, that are produced during casting. In fact, the maximum flaw size has been recognized as the most important parameter in determining the fatigue properties of aluminum shape castings, where generally the larger the maximum flaw size, the lower the fatigue strength for a given fatigue life. In damage tolerant designs, the crack propagation life is estimated from the flaw propagation rate and initial flaw size. In this regard, an example of such fatigue life estimates can be found in a publication by the present inventors entitled Fatigue Life Prediction in Aluminum Shape Castings that was published as part of Simulation of Aluminum Shape Casting Processing: From Alloy Design to Mechanical Properties by The Minerals, Metals & Materials Society in 2006, the contents of which are herein incorporated by reference. In the publication, the present inventors noted that fatigue properties in the presence of casting flaws can be predicted based only on crack propagation, such that crack initiation can be ignored. They also noted that a significant problem with predicting fatigue properties in such a case is defining a starting flaw size.
The present inventors recognize that the lack of a computational tool that takes into consideration different classes or scales of flaw sizes in conjunction with statistical approaches is a limitation on the ability to accurately predict the fatigue properties of castings. As such, there remains a desire for a method and system to predict the fatigue life of aluminum cast components based on accurate assessments of the various flaws over multiple size scales (due to, for example, pores, voids, oxide films or the like), microstructural constituents (for example, second phase particles and aluminum dendritic structures) and submicron scale precipitate structures.